Z
Zhouchen Lin
Researcher at Peking University
Publications - 471
Citations - 25029
Zhouchen Lin is an academic researcher from Peking University. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 65, co-authored 417 publications receiving 19381 citations. Previous affiliations of Zhouchen Lin include Samsung & Microsoft.
Papers
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Journal ArticleDOI
Robust Recovery of Subspace Structures by Low-Rank Representation
TL;DR: It is shown that the convex program associated with LRR solves the subspace clustering problem in the following sense: When the data is clean, LRR exactly recovers the true subspace structures; when the data are contaminated by outliers, it is proved that under certain conditions LRR can exactly recover the row space of the original data.
Proceedings Article
Robust Subspace Segmentation by Low-Rank Representation
TL;DR: Both theoretical and experimental results show that low-rank representation is a promising tool for subspace segmentation from corrupted data.
Proceedings Article
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation
TL;DR: A linearized ADM (LADM) method is proposed by linearizing the quadratic penalty term and adding a proximal term when solving the sub-problems, allowing the penalty to change adaptively according to a novel update rule.
Journal ArticleDOI
Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm
TL;DR: Zhang et al. as mentioned in this paper proposed a tensor robust principal component analysis (TRPCA) model based on the tensor-tensor product (or t-product) to recover the low-rank and sparse components from their sum.
Book ChapterDOI
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
TL;DR: A novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining based on contextual information is proposed and outperforms the state-of-the-art approaches under all evaluation metrics.